SympleGraph: distributed graph processing with precise loop-carried dependency guarantee

Zhuo, Youwei; Chen, Jingji; Luo, Qinyi; Wang, Yanzhi; Yang, Hailong; Qian, Depei; Qian, Xuehai

doi:10.1145/3385412.3385961

Cited by 11 publications

(7 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Message-passing graph processing There are several graph processing systems that provide a message-passing abstraction that run on CPUs [11,20,22,25,33,42,43] and GPUs [9,17,26,31,41]. Our evaluation shows that NextDoor outperforms Tigr [26] on graph sampling tasks (Section 8).…”

Section: Related Workmentioning

confidence: 97%

Accelerating graph sampling for graph machine learning using GPUs

Jangda

Polisetty

Guha

et al. 2021

Proceedings of the Sixteenth European Conference on Computer Systems

View full text Add to dashboard Cite

Representation learning algorithms automatically learn the features of data. Several representation learning algorithms for graph data, such as DeepWalk, node2vec, and Graph-SAGE, sample the graph to produce mini-batches that are suitable for training a DNN. However, sampling time can be a significant fraction of training time, and existing systems do not efficiently parallelize sampling.Sampling is an "embarrassingly parallel" problem and may appear to lend itself to GPU acceleration, but the irregularity of graphs makes it hard to use GPU resources effectively. This paper presents NextDoor, a system designed to effectively perform graph sampling on GPUs. NextDoor employs a new approach to graph sampling that we call transit-parallelism, which allows load balancing and caching of edges. NextDoor provides end-users with a high-level abstraction for writing a variety of graph sampling algorithms. We implement several graph sampling applications, and show that NextDoor runs them orders of magnitude faster than existing systems.

show abstract

Section: Related Workmentioning

confidence: 97%

Accelerating graph sampling for graph machine learning using GPUs

Jangda

Polisetty

Guha

et al. 2021

Proceedings of the Sixteenth European Conference on Computer Systems

View full text Add to dashboard Cite

show abstract

“…SLFE (Song et al 2018) reduces redundancies in vertex computation by utilizing a graph’s topological knowledge on the fly. SympleGraph (Zhuo et al 2020) eliminates unnecessary computations and communications by propagating loop-carried dependency dynamically. Unlike these frameworks, Fregel takes a static optimization approach, but the optimization methods used for Fregel are not new.…”

Section: Related Workmentioning

confidence: 99%

“…The vertex-centric model is particularly promising for avoiding mistakes in parallel programming. It has been intensively studied and has served as the basis for a number of practically useful graph processing systems (McCune et al 2015; DKhan, 2017; Liu & Khan, 2018; Song et al , 2018; Zhuo et al , 2020). We thus focus on the vertex-centric model in this article.…”

Section: Introductionmentioning

confidence: 99%

Fregel: a functional domain-specific language for vertex-centric large-scale graph processing

Iwasaki¹,

Emoto²,

Morihata

et al. 2022

J. Funct. Prog.

View full text Add to dashboard Cite

The vertex-centric programming model is now widely used for processing large graphs. User-defined vertex programs are executed in parallel over every vertex of a graph, but the imperative and explicit message-passing style of existing systems makes defining a vertex program unintuitive and difficult. This article presents Fregel, a purely functional domain-specific language for processing large graphs and describes its model, design, and implementation. Fregel is a subset of Haskell, so Haskell tools can be used to test and debug Fregel programs. The vertex-centric computation is abstracted using compositional programming that uses second-order functions on graphs provided by Fregel. A Fregel program can be compiled into imperative programs for use in the Giraph and Pregel+ vertex-centric frameworks. Fregel’s functional nature without side effects enables various transformations and optimizations during the compilation process. Thus, the programmer is freed from the burden of program optimization, which is manually done for existing imperative systems. Experimental results for typical examples demonstrated that the compiled code can be executed with reasonable and promising performance.

show abstract

“…Although the total external bandwidth between memory cubes is higher than internal bandwidth, recent studies [47,104,106] have shown that the remote communication is still the bottleneck. We can apply ideas in the distributed graph processing such as [118] to further reduce the communication amount and improve performance.…”

Section: Research Directionsmentioning

confidence: 99%

Graph processing and machine learning architectures with emerging memory technologies: a survey

Qian

2021

Sci. China Inf. Sci.

Self Cite

View full text Add to dashboard Cite

This paper surveys domain-specific architectures (DSAs) built from two emerging memory technologies. Hybrid memory cube (HMC) and high bandwidth memory (HBM) can reduce data movement between memory and computation by placing computing logic inside memory dies. On the other hand, the emerging non-volatile memory, metal-oxide resistive random access memory (ReRAM) has been considered as a promising candidate for future memory architecture due to its high density, fast read access and low leakage power. The key feature is ReRAM's capability to perform the inherently parallel in-situ matrixvector multiplication in the analog domain. We focus on the DSAs for two important applications-graph processing and machine learning acceleration. Based on the understanding of the recent architectures and our research experience, we also discuss several potential research directions.

show abstract

SympleGraph: distributed graph processing with precise loop-carried dependency guarantee

Cited by 11 publications

References 37 publications

Accelerating graph sampling for graph machine learning using GPUs

Accelerating graph sampling for graph machine learning using GPUs

Fregel: a functional domain-specific language for vertex-centric large-scale graph processing

Graph processing and machine learning architectures with emerging memory technologies: a survey

Contact Info

Product

Resources

About